The Contribution of ICT to Production Efficiency in Italy
Bibliographic record
Abstract
This paper examines the impact of information and communications technologies (ICTs) on technical production efficiency in a wide range of Italian industries.Technical efficiency, defined as the firm's distance from the production efficiency frontier, is one important component of productivity.Assessing the role of ICTs in the organisation and control of production processes may be of primary interest for those firms that are trying to rationalise their production organisation and techniques.The survey of firms examined, the Italian ISTAT SCI covering all firms with at least 20 employees, offers an opportunity to test the hypothesis that ICTs, in both hardware and software components, can positively influence production performance.The analysis is carried out within industries defined by the OECD STAN database, to ease international comparability of the empirical results.Technical efficiency of each individual firm is measured by means of data envelopment analysis, a non-parametric technique that is well known in the field of operations research.The correlation between ICT and technical efficiency is examined using cross-sectional regressions run on firm-level data within each industry.The main conclusion is that this correlation is not significantly rejected in the majority of the industrial sectors considered.In general, positive correlations are not rejected in all four groups of industries defined on the basis of R&D intensity of production.However, technical efficiency does not seem to be affected by ICT in a significant share of high R&D intensity industries.This paradoxical result can be explained by noting that almost all firms in these industries already operate at high relative levels of technical efficiency; there is little margin for further gains through increases in the ICT intensity of production.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".